Underground sewer pipe condition assessment based on convolutional neural networks-Reference-Cited by-同舟云学术

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Underground sewer pipe condition assessment based on convolutional neural networks

Published:2019-10 Issue: Volume:106 Page:102849
ISSN:0926-5805
Container-title:Automation in Construction
language:en
Short-container-title:Automation in Construction

Author:

Hassan Syed Ibrahim,Dang L. Minh,Mehmood Irfan,Im Suhyeon,Choi Changho,Kang Jaemo,Park Young-Soo,Moon Hyeonjoon

Funder

National Research Council of Science & Technology

Publisher

Elsevier BV

Subject

Building and Construction,Civil and Structural Engineering,Control and Systems Engineering

Reference39 articles.

1. Tafuri, Anthony N., and Ariamalar Selvakumar. "Wastewater collection system infrastructure research needs in the USA." UrbanWater 4 (1) 2002, 21–29. https://doi.org/10.1016/S1462-0758(01)00070-X.

2. Automated diagnosis of sewer pipe defects based on machine learning approaches;Yang;Expert Syst. Appl.,2008

3. Innovative method for assessment of underground sewer pipe condition;Koo;Autom. Constr.,2006

4. Automatic crack detection and classification method for subway tunnel safety monitoring;Zhang;Sensors,2014

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